The 10 best ETL & Data Pipelines courses in 2026

This is a self-paced lab that takes place in the Google Cloud console. In this lab you will build several Data Pipelines that will ingest data from a publicly available dataset into BigQuery.

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course provides hands-on experience with essential AWS data engineering tools and techniques. You’ll work on real-world projects using services like Redshift, DynamoDB, Athena, Glue, Kinesis, and Step Functions to build data pipelines, automate workflows, and process data at scale. Through labs, you'll learn to develop batch and real-time data processing solutions,

This course teaches you how to build and manage data warehouses and pipelines using Microsoft Fabric. You will get hands-on experience by creating data warehouses, designing schemas, and developing ETL/ELT pipelines with Data Factory. The course covers the use of T-SQL for data transformations and incorporates version control for data workflows, ensuring you can manage production environments confidently. By the end of this course, you will be able to orchestrate data pipelines, implement security, and handle data-to-insights workflows from ingestion through security and deployment.

Build production-ready data pipelines using Delta Live Tables and the Medallion Architecture on Databricks. This hands-on course teaches you to design, implement, and monitor ETL workflows that transform raw data into reliable, business-ready datasets through a structured bronze-silver-gold layering pattern. This course is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with professionals with an interest in programming. You will start by mastering DLT fundamentals — declarative pipeline syntax in both SQL and Python, streaming ingestion w

Take a Rust crate from a "works on my machine" laptop build to a production-grade artifact that ships through a gate no one can bypass. The course walks through a real 3-crate ETL workspace — etl-core, etl-cli, and etl-bench — built around clap derive macros, a tuned `[profile.release]`, and a multi-stage Dockerfile that drops a 1.8 GB rust:latest image to a 6 MB scratch+musl container with no shell to attack. You then wire pmat, bashrs, forjar, and pv onto the standard fmt + clippy + test + 100% coverage + audit + deny stack — because a green build badge is misleading when an agent wrote half

Build practical data engineering skills by learning how to design, develop, and execute end-to-end ETL (Extract, Transform, Load) pipelines using Apache Spark. In this hands-on course, you will begin by setting up a Spark development environment, installing and configuring PySpark, Hadoop, and MySQL, organizing ETL project structures, and exploring real-world datasets. As you progress, you will implement complete and incremental ETL workflows using Apache Spark. You'll integrate Spark with MySQL through JDBC, apply data transformation logic with Spark SQL, perform business-rule filtering, and

Build production-grade ETL pipelines in Rust that never drop rows silently and never panic on malformed input. In five weeks you will design the Extract boundary with serde-typed readers, enforce a Transform totality contract where every row either yields a validated record or flows through a structured error channel, and emit NDJSON and CSV from the same typed pipeline using the Write trait. You will master thiserror enums, ? propagation, proptest-based property testing, round-trip invariants, and the stderr-vs-stdout separation that makes every pipeline run auditable. The course closes with

Use SQLite from Rust to build production-grade data tooling — the embedded, single-file SQL engine that ships in every Android phone, every iOS device, and most web browsers. You'll start with the basics of SQLite as a serverless library, then drive it from Rust with the rusqlite crate: opening file-backed and in-memory databases, running INSERT, SELECT, UPDATE, DELETE through prepared statements, and surfacing errors as `Result` rather than swallowing them. Module 2 turns the database into a real ETL stage: stream CSV with the csv crate and serde, ingest JSON with serde_json into typed column

In this course, you will master advanced networking techniques and Swift programming strategies for iOS development. You'll explore how to efficiently fetch and manage network data, build dynamic and interactive graphs using Swift Charts, and implement powerful data pipelines with Combine. These skills are crucial for developing data-driven, high-performance iOS applications. Throughout the course, you'll gain hands-on experience with real-world challenges like managing network data asynchronously and visualizing it with Swift Charts. By mastering these advanced techniques, you'll be able to

In this course, you will explore various types of source systems, learn how they generate and update data, and troubleshoot common issues you might encounter when trying to connect to these systems in the real world. You’ll dive into the details of common ingestion patterns and implement batch and streaming pipelines. You’ll automate and orchestrate your data pipelines using infrastructure as code and pipelines as code tools. You’ll also explore AWS and open source tools for monitoring your data systems and data quality.